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Transfer learning from simulations on a reference anatomy for ECGI in personalized cardiac resynchronization therapy

Research output: Contribution to journalArticlepeer-review

Sophie Giffard-Roisin, Herve Delingette, Thomas Jackson, Jessica Webb, Lauren Fovargue, Jack Lee, C. Aldo Rinaldi, Reza Razavi, Nicholas Ayache, Maxime Sermesant

Original languageEnglish
Article number8362988
Pages (from-to)343-353
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number2
Early online date23 May 2018
Accepted/In press22 May 2018
E-pub ahead of print23 May 2018
PublishedFeb 2019


  • Transfer Learning From Simulations_GIFFARD-ROISIN_Accepted22May2018Publishedonline23May2018_GREEN AAM

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    Uploaded date:08 Jan 2020

    Version:Accepted author manuscript

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King's Authors


Goal: Noninvasive cardiac electrophysiology (EP) model personalisation has raised interest for instance in the scope of predicting EP cardiac resynchronization therapy (CRT) response. However, the restricted clinical applicability of current methods is due in particular to the limitation to simple situations and the important computational cost. Methods: We propose in this manuscript an approach to tackle these two issues. First, we analyze more complex propagation patterns (multiple onsets and scar tissue) using relevance vector regression and shape dimensionality reduction on a large simulated database. Second, this learning is performed offline on a reference anatomy and transferred onto patient-specific anatomies in order to achieve fast personalized predictions online. Results: We evaluated our method on a dataset composed of 20 dyssynchrony patients with a total of 120 different cardiac cycles. The comparison with a commercially available electrocardiographic imaging (ECGI) method shows a good identification of the cardiac activation pattern. From the cardiac parameters estimated in sinus rhythm, we predicted five different paced patterns for each patient. The comparison with the body surface potential mappings (BSPM) measured during pacing and the ECGI method indicates a good predictive power. Conclusion: We showed that learning offline from a large simulated database on a reference anatomy was able to capture the main cardiac EP characteristics from noninvasive measurements for fast patient-specific predictions. Significance: The fast CRT pacing predictions are a step forward to a noninvasive CRT patient selection and therapy optimisation, to help clinicians in these difficult tasks.

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